Multisensor fusion estimation theory and application / Liping Yan, Lu Jiang, Yuanqing Xia.
Yan, Liping, author.; Jiang, Lu, author.; Xia, Yuanqing, author.
2021
TK7872.D48
Available Online

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Title Multisensor fusion estimation theory and application / Liping Yan, Lu Jiang, Yuanqing Xia.
Author Yan, Liping, author.
ISBN 9789811594267 (electronic bk.)
9811594260 (electronic bk.)
9811594252
9789811594250
DOI https://doi.org/10.1007/978-981-15-9426-7
Published Singapore : Springer, [2021]
Language English
Description 1 online resource (xvii, 227 pages) : illustrations (some color)
Other Standard Identifiers 10.1007/978-981-15-9426-7 doi
Call Number TK7872.D48
Dewey Decimal Classification 681/.2
621.382
Summary This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed January 22, 2021).
Added Author Jiang, Lu, author.
Xia, Yuanqing, author.
Available in Other Form Print version: 9811594252
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All Resources
Introduction to Optimal Fusion Estimation and Kalman Filtering: Preliminaries
Kalman Filtering of Discrete Dynamic Systems
Optimal Kalman filtering Fusion for Linear Dynamic Systems with Cross-Correlated Sensor Noises
Distributed Data Fusion for Multirate Sensor Networks
Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise
Fusion Estimation for Asynchronous Multirate Multisensor Systems with Unreliable Measurements and Coupled Noises
Multi-sensor Distributed Fusion Estimation for Systems with Network Delays, Uncertainties and Correlated Noises
Event-triggered Centralized Fusion Estimation for Dynamic Systems with Correlated Noises
Event-triggered Distributed Fusion Estimation for WSN Systems
Event-triggered Sequential Fusion Estimation for Dynamic Systems with Correlated Noises
Distributed Fusion Estimation for Multisensor Systems with Heavy-tailed Noises
Sequential Fusion Estimation for Multisensor Systems with Heavy-tailed Noises.

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